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Mining frequent sequential patterns with first-occurrence forests
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Source ACM Southeast Regional Conference archive
Proceedings of the 46th Annual Southeast Regional Conference on XX table of contents
Auburn, Alabama
SESSION: Data mining and database systems table of contents
Pages 34-39  
Year of Publication: 2008
ISBN:978-1-60558-105-7
Authors
Erich A. Peterson  University of Arkansas at Little Rock, Little Rock, AR
Peiyi Tang  University of Arkansas at Little Rock, Little Rock, AR
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 8,   Downloads (12 Months): 21,   Citation Count: 1
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ABSTRACT

In this paper, a new pattern-growth algorithm is presented to mine frequent sequential patterns using First-Occurrence Forests (FOF). This algorithm uses a simple list of pointers to the first-occurrences of a symbol in the aggregate tree [1], as the basic data structure for database representation, and does not rebuild aggregate trees for projection databases. The experimental evaluation shows that our new FOF mining algorithm outperforms the PLWAP-tree mining algorithm [2] and the FLWAP-tree mining algorithm [3], both in the mining time and the amount of memory used.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Peiyi Tang, Markus P. Turkia, and Kyle A. Gallivan. Mining web access patterns with first-occurrence linked WAP-trees. In Proceedings of the 16th International Conference on Software Engineering and Data Engineering (SEDE'07), pages 247--252, Las Vegas, USA, July 2007.
 
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Collaborative Colleagues:
Erich A. Peterson: colleagues
Peiyi Tang: colleagues